Abstract
Proper load models are essential to power system stability analysis. This paper proposes a methodology for the development of neural network (NN) based composite load models for power system stability analysis. A two-step modeling procedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the distribution level. Then, the test bed data is used to develop a composite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controling an induction motor load is presented.
| Original language | English (US) |
|---|---|
| Title of host publication | 2004 IEEE PES Power Systems Conference and Exposition |
| Pages | 1159-1163 |
| Number of pages | 5 |
| State | Published - Dec 1 2004 |
| Event | 2004 IEEE PES Power Systems Conference and Exposition - New York, NY, United States Duration: Oct 10 2004 → Oct 13 2004 |
Publication series
| Name | 2004 IEEE PES Power Systems Conference and Exposition |
|---|---|
| Volume | 2 |
Other
| Other | 2004 IEEE PES Power Systems Conference and Exposition |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 10/10/04 → 10/13/04 |
Fingerprint
Keywords
- Artificial neural networks
- Composite load modeling
- Power systems
- Stability analysis
ASJC Scopus subject areas
- Engineering(all)
Cite this
Keyhani, A., Lu, W., & Heydt, G. T. (2004). Composite neural network load models for power system stability analysis. In 2004 IEEE PES Power Systems Conference and Exposition (pp. 1159-1163). (2004 IEEE PES Power Systems Conference and Exposition; Vol. 2).